Vines and Wines by Zach Dischner
Date: April 4 2017 Part of the Udacity Data Analyst Nanodegree program
Red Wines
Dataset Description
This investigaion is about red wine. It will analyze red wine properties in order to correlate chemical components to overall wine quality.
Data Structure
## 'data.frame': 1599 obs. of 14 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ fixed.acidity : num 7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 7.5 ...
## $ volatile.acidity : num 0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.5 ...
## $ citric.acid : num 0 0 0.04 0.56 0 0 0.06 0 0.02 0.36 ...
## $ residual.sugar : num 1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 6.1 ...
## $ chlorides : num 0.076 0.098 0.092 0.075 0.076 0.075 0.069 0.065 0.073 0.071 ...
## $ free.sulfur.dioxide : num 11 25 15 17 11 13 15 15 9 17 ...
## $ total.sulfur.dioxide: num 34 67 54 60 34 40 59 21 18 102 ...
## $ density : num 0.998 0.997 0.997 0.998 0.998 ...
## $ pH : num 3.51 3.2 3.26 3.16 3.51 3.51 3.3 3.39 3.36 3.35 ...
## $ sulphates : num 0.56 0.68 0.65 0.58 0.56 0.56 0.46 0.47 0.57 0.8 ...
## $ alcohol : num 9.4 9.8 9.8 9.8 9.4 9.4 9.4 10 9.5 10.5 ...
## $ quality : int 5 5 5 6 5 5 5 7 7 5 ...
## $ total.acidity : num 8.1 8.68 8.56 11.48 8.1 ...Data Summary
## X fixed.acidity volatile.acidity citric.acid
## Min. : 1.0 Min. : 4.60 Min. :0.1200 Min. :0.000
## 1st Qu.: 400.5 1st Qu.: 7.10 1st Qu.:0.3900 1st Qu.:0.090
## Median : 800.0 Median : 7.90 Median :0.5200 Median :0.260
## Mean : 800.0 Mean : 8.32 Mean :0.5278 Mean :0.271
## 3rd Qu.:1199.5 3rd Qu.: 9.20 3rd Qu.:0.6400 3rd Qu.:0.420
## Max. :1599.0 Max. :15.90 Max. :1.5800 Max. :1.000
## residual.sugar chlorides free.sulfur.dioxide
## Min. : 0.900 Min. :0.01200 Min. : 1.00
## 1st Qu.: 1.900 1st Qu.:0.07000 1st Qu.: 7.00
## Median : 2.200 Median :0.07900 Median :14.00
## Mean : 2.539 Mean :0.08747 Mean :15.87
## 3rd Qu.: 2.600 3rd Qu.:0.09000 3rd Qu.:21.00
## Max. :15.500 Max. :0.61100 Max. :72.00
## total.sulfur.dioxide density pH sulphates
## Min. : 6.00 Min. :0.9901 Min. :2.740 Min. :0.3300
## 1st Qu.: 22.00 1st Qu.:0.9956 1st Qu.:3.210 1st Qu.:0.5500
## Median : 38.00 Median :0.9968 Median :3.310 Median :0.6200
## Mean : 46.47 Mean :0.9967 Mean :3.311 Mean :0.6581
## 3rd Qu.: 62.00 3rd Qu.:0.9978 3rd Qu.:3.400 3rd Qu.:0.7300
## Max. :289.00 Max. :1.0037 Max. :4.010 Max. :2.0000
## alcohol quality total.acidity
## Min. : 8.40 Min. :3.000 Min. : 5.120
## 1st Qu.: 9.50 1st Qu.:5.000 1st Qu.: 7.680
## Median :10.20 Median :6.000 Median : 8.445
## Mean :10.42 Mean :5.636 Mean : 8.847
## 3rd Qu.:11.10 3rd Qu.:6.000 3rd Qu.: 9.740
## Max. :14.90 Max. :8.000 Max. :16.285Observations of Summary Data:
- There are 1599 samples of Red Wine properties and quality values
- No wine achieves either a terrible (0) or perfect (10) quality score
- Residual Sugar measurement has a maximum that is nearly 19 times farther away
from the 3rd quartile than the 3rd quartile is from the 1st. - Citric Acid had a minimum of 0.0. No other property values was exactly 0.
Univariate Plots Section
Overall distribution of quality rating of red wines. Quality here is a single measurement between 1 and 10 of oerall wine quality, as decided by three separate agencies
Overall wine quality, rated on a scale from 1 to 10, has a normal shape and very few exceptionally high or low quality ratings.
Observations of Univariate Properties:
- pH distribution is roughly normal centered around 3.3 with tails at 3.0 and
3.6 - Sulphates look fairly normal centered at 0.6 +- 0.3, but with a positive skew
that has measurements all the way out to 2.0 - Chlorides show a similar behavior with a center around 0.075
- Citric acid looks nearly like a double ramp function, with a peak at 0.0 and
nearly constantly decreasing values until another peak citric acid value at
0.25. A third jump at 0.5 is notable as well. - Total acidity is roughly normal with a center at 7.5 and bounds at approx 5.0
and 15.0.
Of all single component distributions, pH appears to be the only measurement that is normally distributed. Acidity is next, being normal with just a slight positive skew. Citric Acid and S02 particularly exhibit a strong positive skew. pH and Acidity reflect measurements that are centered around non-zero numbers, whereas the other measurements are clustered at near-zero numbers. Since these measurements cannot have negative values, the skewed distribution is sensible.
Note on Acidity Wine acidity is measured in multiple components: volatile and static. Total acidity is not to be confused with the pH of a wine. The former details the amount of acid in the wine, the latter relates to the strength of the acids.
More on static and volatile acidity measures here: http://winemakersacademy.com/understanding-wine-acidity/
Univariate Analysis
Overview
The red wine dataset features 1599 separate observations, each for a different red wine sample. For each sample, 11 laboratory based measurements were made, as well as a single subjective overall quality rating. The quality of a wine was decided upon by three separate professional wine assessment institutions.
As presented, each wine sample is provided as a single row in the dataset. Due to the nature of how some measurements are gathered, some values given represent components of a measurement total. For example, data.fixed.acidity and data.volatile.acidity are both obtained via separate measurement techniques, and must be summed to indicate the total acidity present in a wine sample. For these cases, I supplimented the data given by computing the total and storing in the dataframe with a data.total.* variable.
Features of Interest
The main interesting measurement here is the wine quality. It is the subjective measurement of how attractive the wine might be to a consumer. The goal here will be to try and correlate non-subjective wine properties with its quality.
I am curious about a few trends in particular:
- sulphates vs quality - Low sulphate wine has a reputation for not causing
hangovers - alcohol vs quality - Just an interesting measurement. Strong beers are
typically higher quality and harder to make than lower proof beers.
At first, the lack of an age metric might be surprising since it is commonly a factor in quick assumptions of wine quality. However, since the actual effect of wine age is on the wine’s measurable chemical properties, its inclusion here is not necessary.
Distributions
I left distributions alone here. Many measurements that were clustered close to zero had a positive skew (you can’t have negative percentages or ammounts). Others such as pH and total.acidity and quality had normal looking distributions.
Bivariate Plots Section
Scatterplot Matrix
Observations of Correlation Plot Matrix
- Total Acidity is highly correlatable with fixed acidity
- pH appears correlatable with acidity, citric acid, chlorides, and residual
sugars - No single property appears to have a correlation with quality
Sulphates
q1 <- ggplot(aes(x=sulphates, y=quality), data=data) +
geom_jitter(alpha=2/3) +
geom_smooth() +
ggtitle("Sulphates vs Quality")
q2 <- ggplot(aes(x=sulphates, y=quality), data=subset(data, data$sulphates < 1)) +
geom_jitter(alpha=2/3) +
geom_smooth() +
ggtitle("Sulphates vs Quality without outliers")
grid.arrange(q1,q2, ncol=1)
Observations:
- There is a slight trend implying a relationship between sulphates and wine
qualtity, particularly if you disregard extreme sulphate values - Disregarding measurements where sulphates > 1.0 is the same as disregarding
the positive tail of the distribution, keeping just the normal-looking portion - The relationship is still a week wone
Alcohol
q0 <- ggplot(aes(x=alcohol, y=quality), data=data) +
geom_jitter(alpha=2/3) +
geom_smooth() +
ggtitle("Alcohol Content vs Quality")
q1 <- ggplot(aes(x=alcohol), data=data) +
geom_density(fill=I("#BB0000")) +
facet_wrap("quality") +
ggtitle("Alcohol Content for Wine Quality Ratings")
q2 <- ggplot(aes(x=residual.sugar, y=alcohol), data=data) +
geom_point(alpha=2/3) +
geom_smooth() +
ggtitle("Alcohol vs Residual Sugar Content")
grid.arrange(q1, arrangeGrob(q0, q2), ncol = 2)
Observations
- Alcohol and quality appear to be somewha correlatable
- Lower quality wines tended to have lower alcohol contents
- Higher quality wines tended to have progressively higher alcohol content
- There is no/an erratic relation between sugar and alcohol content, which I
found surprising as alcohol is a byproduct of the yeast feeding off of sugar
during the fermentation process
SO2, Chlorides, Sugar, and Density
q1 <- ggplot(aes(x=total.sulfur.dioxide, y=quality),
data=subset(data, data$total.sulfur.dioxide <
quantile(total.sulfur.dioxide, 0.99))) +
geom_jitter(alpha=1/3) +
geom_smooth() + ggtitle("SO2")
q2 <- ggplot(aes(x=chlorides, y=quality),data=data) +
geom_jitter(alpha=1/3) +
geom_smooth() + ggtitle("Chlorides")
q3 <- ggplot(aes(x=residual.sugar, y=quality),data=data) +
geom_jitter(alpha=1/3) +
geom_smooth() + ggtitle("Residual Sugar")
q4 <- ggplot(aes(x=total.acidity, y=quality),data=data) +
geom_jitter(alpha=1/3) +
geom_smooth() + ggtitle("Total Acidity")
grid.arrange(q1,q2, q3, q4)
Observations
- There is little to no noticeable correlation between S02 and wine quality
- Residual Sugar has a particularly weak correlation with wine quality
- Acidity has no noticeable correlation with wine quality. This
surprised me more than any other trend
Bivariate Analysis
Strong Correlations
I did not find a single, obvious and strong correlation between any wine property and the given quality. Alcohol content is a strong contender but even so, the correlation wasn’t particularly strong.
Notables
Most properties have roughly normal distributions, with some skew in one tail. Scatterplot relationships between these properties often showed a slight trend within the bulk of property values. However, as soon as the we leave the expected range, the trends actually reverse. See Alcohol Content or Sulphate vs Quality for examples. The trend isn’t a definitive one, but it is seen in different variables. Possibly, obtaining an outlier property (say sulphate content) is particularly difficult to do in the wine making process. Or, the wines that exhibit outlier properties are deliberatly of a non-standard variety. In that case, it could be that wine judges have a harder time agreeing on a quality rating
Multivariate Plots Section
Observtions
- Adding chlorides to the Alcohol vs Quality added little insight to the plot
- Unusually bright chloride points occured at different qualities and alcohol
content points with no discernable pattern - Higher alcohol content and lower chloride content appears to correlate to
higher quality wines - Higher alcohol content and higher citric acid content appears to correlate to
higher quality wines - pH has no notable effect on wine quality
Observations
- Higher quality wines appear to have a slight correlation with higher acidity
across all densities - There are abnormally high and low quality wines coincident with higher-than
-usual sugar content.
Model Creation
m1 <- lm((quality ~ alcohol), data = data)
m2 <- update(m1, ~ . + citric.acid)
m3 <- update(m2, ~ . + chlorides)
m4 <- update(m3, ~ . + residual.sugar)
m5 <- update(m4, ~ . + total.acidity)
mtable(m1, m2, m3, m4, m5)##
## Calls:
## m1: lm(formula = (quality ~ alcohol), data = data)
## m2: lm(formula = quality ~ alcohol + citric.acid, data = data)
## m3: lm(formula = quality ~ alcohol + citric.acid + chlorides, data = data)
## m4: lm(formula = quality ~ alcohol + citric.acid + chlorides + residual.sugar,
## data = data)
## m5: lm(formula = quality ~ alcohol + citric.acid + chlorides + residual.sugar +
## total.acidity, data = data)
##
## =========================================================================
## m1 m2 m3 m4 m5
## -------------------------------------------------------------------------
## (Intercept) 1.875*** 1.830*** 2.056*** 2.085*** 2.000***
## (0.175) (0.171) (0.186) (0.187) (0.233)
## alcohol 0.361*** 0.346*** 0.333*** 0.334*** 0.336***
## (0.017) (0.016) (0.017) (0.017) (0.017)
## citric.acid 0.730*** 0.798*** 0.814*** 0.767***
## (0.090) (0.092) (0.093) (0.121)
## chlorides -1.218** -1.200** -1.179**
## (0.389) (0.390) (0.391)
## residual.sugar -0.017 -0.017
## (0.012) (0.012)
## total.acidity 0.008
## (0.013)
## -------------------------------------------------------------------------
## R-squared 0.2 0.3 0.3 0.3 0.3
## adj. R-squared 0.2 0.3 0.3 0.3 0.3
## sigma 0.7 0.7 0.7 0.7 0.7
## F 468.3 276.6 188.7 142.0 113.6
## p 0.0 0.0 0.0 0.0 0.0
## Log-likelihood -1721.1 -1688.7 -1683.8 -1682.9 -1682.7
## Deviance 805.9 773.9 769.2 768.3 768.2
## AIC 3448.1 3385.4 3377.6 3377.8 3379.5
## BIC 3464.2 3406.9 3404.5 3410.1 3417.1
## N 1599 1599 1599 1599 1599
## =========================================================================data$guess=median(data$quality)
m21 <- lm((guess ~ quality), data = data)
m22 <- update(m21, ~ . + alcohol)
m23 <- update(m22, ~ . + chlorides)
mtable(m21, m22, m23)##
## Calls:
## m21: lm(formula = (guess ~ quality), data = data)
## m22: lm(formula = guess ~ quality + alcohol, data = data)
## m23: lm(formula = guess ~ quality + alcohol + chlorides, data = data)
##
## ===================================================
## m21 m22 m23
## ---------------------------------------------------
## (Intercept) 6.000*** 6.000*** 6.000***
## (0.000) (0.000) (0.000)
## quality 0.000 0.000 0.000
## (0.000) (0.000) (0.000)
## alcohol 0.000 0.000
## (0.000) (0.000)
## chlorides 0.000
## (0.000)
## ---------------------------------------------------
## R-squared 0.5 0.5 0.5
## adj. R-squared 0.5 0.5 0.5
## sigma 0.0 0.0 0.0
## F 1592.5 796.0 530.4
## p 0.0 0.0 0.0
## Log-likelihood 50645.6 50645.8 50646.0
## Deviance 0.0 0.0 0.0
## AIC -101285.2 -101283.7 -101281.9
## BIC -101269.1 -101262.2 -101255.0
## N 1599 1599 1599
## ===================================================Multivariate Analysis
The strongest relationship between wine properties and wine quality was that of alcohol, chlorides and citric acid. Typically, pH is considered important when assessing wine quality, however the data does not show an appreciable correlation. In fact, any correlations were week ones. The model built with alcohol, chlorides, citric acid, residual sugar and total acidity featured a low R-squared value of just 0.3. Compared with a model where we just start with a guess of the median wine quality (R-squared of 0.5), the model performs rather poorly.
Often, the tails of property distributions showed a varied relationship with quality. Sometimes, the tails would even reverse trends exhibited by the bulk of the property distribution
I was most surprised that pH and residual sugar had no appreciable effect on wine quality as these are two factors that I personally have heard when sommeliers discuss a wine. ——
Final Plots and Summary
Plot One
Description One
This plot I think paints the most complete picture of the analysis of wine quality with two takeaways.
- Alcohol content has some effect on wine quality rating
- The effect is not a strong one, and even for a given rating, there is
significant variability
This visualization was especially appealing to me because of the way that you can almost see the distribution shuffle from left to right as wine ratings increase. Again, just showing a general tendancy instead of a strong correlation.
Plot Two
Description Two
This plot illustrates that (to my suprise) there was little to no correlation between wine quality and pH. At the same time, there a trend where more highly rated wines (brighter blue) tend to be grouped at higher Citric Acid values. However, the trend is far from definite. There are very highly rated wines at low citric acid values as well as very lowely rated wines at high citric acid values.
In summary, this plot illustrates one of the main themes in wine analys thus far: There are observable correlations between wine chemical properties and quality. However, in each case, there are outlier ratings that undermine the general relationship.
Plot Three
Description Three
After alcohol content, citric acid seemed to have the most promeneant effect on wine quality. There is a specific bump at around 0.25 where the wine quality jumps up a rating. That bump was also present in the univariate distribution plot for citric acid. Is that a feature of certain kinds of wine? Or of wines of a certain age? Possibly, just a “sweet spot” of citric acid content that makes for a higher quality wine? The root cause is unknown but I found the behavior interesting nontheless.
When plotted against wine quality in a scatter plot like this, only citric acid and alcohol displayed a noticeable trend. In each case, the distribution is slightly skewed towards higher citric acid and higher quality.
Reflection
Overall, I was initially surprised by the seemingly dispersed nature of the wine data. Nothing was immediately correlatable to being an inherant quality of good wines. However, upon reflection, this is a sensible finding. Wine making is still something of a science and an art, and if there was one single property or process that continually yielded high quality wines, the field wouldn’t be what it is.
I was surprised to find that alcohol content and citric acid were the most correlatable properties to wine quality. In my mind, sulphates and acidity were what I assumed would be the main correlations.
In the future, I would like to do some research into the wine making process. Some winemakers might actively try for some property values or combinations, and finding those combinations (of 3 or more properties) might be the key to truly predicting wine quality. This investigation was not able to find a strong set of two properties that would consistently be able to predict wine quality with any degree of certainty.
Additionally, having the wine type would be helpful for further analysis. Somalliers might prefer certain types of wines to have different properties and behaviors. For example, a Port (as sweet desert wine) surely is rated differently from a dark and robust abernet Sauvignon, which is rated differently from a bright and fruity Syrah. Without knowing the type of wine, it is entirely possible that we are almost literally comparing apples to oranges and can’t find a correlation.
Tip: Here’s the final step! Reflect on the exploration you performed and the insights you found. What were some of the struggles that you went through? What went well? What was surprising? Make sure you include an insight into future work that could be done with the dataset.